Dynamic pathway linking Pakistan flooding to East Asian heatwaves

In July to August 2022, Pakistan suffered historic flooding while record-breaking heatwaves swept southern China, causing severe socioeconomic impacts. Similar extreme events have frequently coincided between two regions during the past 44 years, but the underlying mechanisms remain unclear. Using observations and a suite of model experiments, here, we show that the upper-tropospheric divergent wind induced by convective heating over Pakistan excites a barotropic anomalous anticyclone over eastern China, which further leads to persistent heatwaves. Atmospheric model ensemble simulation indicates that this dynamic pathway linking Pakistan flooding and East Asian heatwaves is intrinsic to the climate system, largely independent of global sea surface temperature forcing. This dynamic connection is most active during July to August when convective variability is large over Pakistan and the associated divergent flow excites barotropic Rossby waves that propagate eastward along the upper troposphere westerly waveguide. This robust waveguide and the time delay offer hopes for improved subseasonal prediction of extreme events in East Asia.


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Figs. S1 to S9 Tables S1 to S3 Fig. S1.Coupled pattern of PNWI flooding and East Asia heatwaves in two other datasets (see Materials and Methods).(A) Results for July through August averaged Pakistan-northwest India (PNWI; 60°E-80°E, 20°N-35°N) outgoing longwave radiation (OLR; contours) and East Asia heatwaves (shading) that accompany the first mode of maximum covariance analysis for 1979-2019.(B) Time series of the OLR (blue line) and heatwave (red line) patterns, with ±0.6 standard deviation shaded.In (A), gray dashed curves represent the Tibetan Plateau (TP).The squared covariance fraction (SCF) and temporal correlation (R) are denoted at the top of (B).The correlations of temporal coefficients for the OLR (blue lines) and heatwave (red lines) between two suites of datasets are denoted at the top of panel (B).Endings with two asterisks indicate that the correlation is 99% significant in (B).

Fig. S2.
Middle and low-level circulation patterns associated with the coupled mode of Pakistan flooding and East Asia heatwaves.Composite pattern differences of horizontal fields for wind (vectors; m s -1 ) and geopotential (shading; gpm) for (A) at 500 hPa, and (B) at 850 hPa.The dotted areas and shown vectors indicate that the differences are significant at the 95% confidence level.The TP is denoted by the gray dashed curves in (A) and the gray shaded area in (B).

Fig. S4.
Global SST-forced pattern and atmospheric internal pattern in the Northern Hemisphere.Correlations between averaged PNWI OLR (red dashed rectangle; multiplied by -1; W m -2 ) and gridded surface temperature (K) over East Asia from July through August for the (A) ensemble mean, and (B) ensemble spread.Dotted regions denote results significant at the 95% confidence level.([10, 15, 20] m/s green contours) and the prescribed diabatic heating (shading; K day -1 ) at 450 hPa (left panels), and the vertical profiles of the heating rate in the heating center for (A) Exp. 1, (B) Exp. 2, and (C) Exp. 3.
Table S1.Years included to the positive and negative groups in the composite analysis.

Fig. S5 .
Fig. S5.Modeled relation with global SST in 10 individual members of AMIP ensemble.Correlations between the July through August averaged gridded SST with (A) PNWI OLR (red dashed rectangle; multiplied by -1; W m -2 ) and (B) YRV surface temperature (K) in the individual members.Dotted regions denote results significant at the 95% confidence level.

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Fig. S6.Global SST-forced pattern and atmospheric internal pattern in June.Correlations between averaged PNWI OLR (red dashed rectangle; multiplied by -1; W m -2 ) and gridded surface temperature (K) over East Asia during June for the (A) ensemble mean, and (B) ensemble spread.The correlations between averaged PNWI OLR and YRV (blue dashed rectangle) surface temperature are denoted in the upper right of panels (A and B).Dotted regions denote results significant at the 95% confidence level.

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Fig. S7.Climatology and variability of Indian summer monsoon convection in an atmospheric model run.(A to C) Monthly mean OLR (shading; W m -2 ) over the Indian summer monsoon region during 1901-2014.(D to F) Dominant patterns from monthly empirical orthogonal function (EOF) decomposition for Indian summer monsoon OLR during 1901-2014.Explained variances of the leading modes are denoted at the top of panels (D to F).

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Fig. S8.Configurations in the three linear baroclinic model experiments.Climatological westerly jet([10, 15, 20]  m/s green contours) and the prescribed diabatic heating (shading; K day -1 ) at 450 hPa (left panels), and the vertical profiles of the heating rate in the heating center for (A) Exp. 1, (B) Exp. 2, and (C) Exp. 3.

Table S2 . Observed correlations and partial correlations after removal of the signal of PNWI rainfall between El Niño-Southern Oscillation (ENSO) and YRV heatwaves for 1979-2014.
Four precipitation datasets (see Methods) are used for validation.The correlations reaching ±0.33/±0.39 in TableS2are at a 95%/99% confidence interval.